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American health care is undergoing a data-driven transformation — and Intermountain Healthcare is leading the way. This MIT Sloan Management Review case study examines the data and analytics culture at Intermountain, a Utah-based company that runs 22 hospitals and 185 clinics. Data-driven decision making has improved patient outcomes in Intermountain’s cardiovascular medicine, endocrinology, surgery, obstetrics and care processes — while saving millions of dollars in procurement and in its supply chain. The case study includes video clips of interviews and a downloadable PDF version.

A panel of experts discusses the challenges of finding, engaging and organizing data scientists for best results. They talk about how to support your data scientists and keep them engaged in the right kinds of tasks and how to integrate new talent into your existing data and analytics team. They also talk about the skills and traits to look for when recruiting and selecting your data/analytics team, and how to assess existing internal talent for data roles.

While analytical skills are improving among managers, the increasing sophistication of analyses is outpacing the development of those skills. The resulting gap creates a need for managers to become comfortable applying analytical results they do not fully understand. A 2014 survey by MIT Sloan Management Review, in partnership with SAS Institute Inc., highlights the ways that companies can address this problem by focusing on both the production and consumption sides of analytics.

Advanced digital technologies are swiftly changing the kinds of skills that jobs require. Researchers Frank MacCrory, George Westerman and Erik Brynjolfsson from the MIT Sloan School of Management and Yousef Alhammadi of the Masdar Institute studied the changes in skill requirements over the 2006-2014 time period. While demand has clearly grown for computer skills, it has grown for interpersonal skills, too. The authors advise people in all lines of work to be flexible about acquiring new talents.

For recruiters, the technological developments of the past 3 years have been transformational, says Tuck Rickards of Russell Reynolds. With the transformation of business to a more real-time, connected, data-driven focus, the type of talent companies seek — even the type of organizational structure they’re building — has undergone a quantum shift. But the changes aren’t yet done: “The next five years are huge for companies to reorient themselves from a leadership and team perspective,” warns Rickards.

In a conversation with MIT Sloan Management Review, Michelle McKenna-Doyle, the NFL’s senior vice president and first-ever CIO, discusses the organization’s customer-focused approach to big data and analytics. She explains how the NFL works to make its employees comfortable with their own data sets.

Hal Varian, chief economist at Google and emeritus professor at UC Berkeley, has been with Google for more than a decade and has unique insight into the past and future of data analytics. In a conversation with MIT Sloan Management Review guest editor Sam Ransbotham, Varian says that companies need to beef up their systems to function within an overwhelming data flow — including new voice-command system data and other computer-mediated transactions.

If you’re lying awake at night fretting that your competition has mastered analytics when you haven’t, take a breath — many of the stories we hear about analytics success are likely skewed. The transition to analytics-focused business is still far, far from universal, and that, says information systems expert Sam Ransbotham, means you have a chance to catch up.

Companies are having a tough time finding the data scientists they need — they just aren’t being trained fast enough to meet market demand. While it may be challenging to keep ambitious analytics projects in development without employees with the necessary skill sets, that doesn’t mean those projects need to halt altogether. Sam Ransbotham offers seven tips for making progress when you don’t have enough analytics talent on board.

Everyone wants to hire skilled data scientists — especially Spain’s Amadeus, a travel sector technology company. Amadeus has brought more than forty new hires into this post since 2013. But locating talent is just the beginning. In an interview with MIT Sloan Management Review, Amadeus’s Denis Arnaud describes the steps he takes to not only identify data science talent, but to make sure they integrate well into the company, too.

When Michael Rappa proposed to his employer, North Carolina State University, that they create a degree program for business analytics in 1999, they dismissed the idea. But 14 years later, the Institute for Advanced Analytics is a pioneering and successful program that trains analytics professionals for businesses hungry for analytics skills. Rappa sat down with MIT Sloan Management Review’s Michael Fitzgerald to explain how the Institute came about — and where it’s headed.

Data scientists differ from other types of analysts in significant respects. To create real business value, top management must learn how to manage these “numbers people” effectively. To help executives avoid repeating some of the mistakes that have undermined the success of previous generations of analytical talent, the authors offer up seven recommendations for providing useful leadership and direction.

The idea is simple: develop a methodology that ties patient outcomes to provider fees so that clinicians are rewarded when patients’ health improves. Making it happen is a lot more complicated. When WellPoint undertook this task, it discovered that there was more to it than simply the challenge of applying data analytics technology — the company’s innovation processes had to be reinvented.

It’s no secret that the fee-for-service model in U.S. health care is a driving factor in spiraling costs. WellPoint’s innovative plan to shift to a value-based payment plan may prove to be a key innovation that keeps a lid on those costs. But as commentator Sam Ransbotham points out, their effort to change the payment system also highlights a need for process changes at WellPoint itself.

Based on a global executive survey with 2,000+ respondents and interviews with more than thirty executives, MIT Sloan Management Review and SAS Institute Inc. report that analytics has become a common path to business value. Organizations are now being challenged to step up their use of analytics, whether they are just getting started or are seasoned practitioners. The implications for industry competition are coming into focus—companies that incorporate analytics into their culture are finding success in the new digital era.

A new book by Thomas H. Davenport and Jinho Kim says that if companies can’t turn all the data they’re swimming in into better decision making through quantitative analytics, they’re “probably creating suboptimal performance.” The book, Keeping Up with the Quants: Your Guide to Understanding and Using Analytics, is geared toward executives who are not analytics experts but whose jobs require them to deal with those who have such expertise, both inside and outside their organizations.

Researchers are proposing a new method to limit privacy harms from predictive analytics: Apply due process that would determine, legally, the fairness of an algorithm. While a new framework may be a step forward for individual privacy, what does it mean for organizations that collect and utilize big data through a predictive analytics lens? A couple of things — should data-oriented due process pass policy and legislative muster.

The growing importance of algorithms to business and society is a little discussed feature of our increasingly digital world. These algorithms are the underpinnings of NSA surveillance, online search engines, corporate security, modern matchmaking and other activities in both the private and public sector. They can be a source of competitive advantage (think Google), play an operational role or drive marketing. Just what are algorithms, how are they used, and what happens when influential algorithms go wrong?

It is an understatement to say LinkedIn is growing like a weed. With 238 million members in over 200 countries, 2.8 million active company profiles, and 1 million professionally oriented groups, LinkedIn has become the world’s largest professional networking site. Deepak Agarwal, LinkedIn’s director of relevance science, explains how his company uses data and analytics to sustain this growth.